System and method for task-less mapping of brain activity

ABSTRACT

A computing device for use in a system for mapping brain activity of a subject includes a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.16/383,926 filed Apr. 15, 2019 and incorporated herein by reference inits entirety, U.S. application Ser. No. 16/383,926 is a continuation ofU.S. application Ser. No. 16/136,996, filed Sep. 20, 2018, patented asU.S. Pat. No. 10,258,289 and incorporated herein by reference in itsentirety. U.S. application Ser. No. 16/136,996 is a continuation of U.S.application Ser. No. 15/237,202, filed Aug. 15, 2016, patented as U.S.Pat. No. 10,092,246 and incorporated herein by reference in itsentirety. U.S. application Ser. No. 15/237,202 is a continuation of U.S.application Ser. No. 13/673,816, filed Nov. 9, 2012, patented as U.S.Pat. No. 9,480,402 and incorporated herein by reference in its entirety.U.S. application Ser. No. 13/673,816 claims priority to U.S. ProvisionalApplication Ser. No. 61/558,751, filed Nov. 11, 2011, and alsoincorporated herein by reference in its entirety.

BACKGROUND

The field of the invention relates generally to brain mapping systemsand, more particularly, to systems and methods for task-less mapping ofbrain activity using resting state data collected from a brain of asubject.

Brain mapping includes a set of neuroscience techniques that arepredicated on the mapping of biological quantities or properties ontospatial representations of a subject's brain resulting in at least onemap. At least some known neuroimaging systems or techniques are usedfrequently in clinical and research settings for brain mapping such thatbrain function can be monitored. For example, functional magneticresonance imaging (fMRI) may be used to enable researchers andclinicians to see visual images of the brain, wherein the images may beused to identify brain activity within a plurality of networks of thebrain. One approach includes a task based technique wherein the fMRI maybe used to detect correlations between brain activation and varioustasks that a subject performs during a scan. Such task-based techniquescan be useful in clinical applications. For example, the images obtainedthrough fMRI may enable a surgeon to identify portions of the brain thatare responsible for various functions and the surgeon may attempt toavoid contact with such portions while performing surgery on the brain.

However, task-based neuroimaging may not be suitable for all segments ofa clinical population. For example, a toddler or a nervous patient maybe unable to comprehend and/or perform various tasks. It was recentlydiscovered, via fMRI, that even during the absence of overt tasks,fluctuations in brain activity are correlated acrossfunctionally-related cortical regions. Thus, the spatial and temporalevaluations of spontaneous neuronal activity has allowed mapping ofthese resting-state networks (RSNs) with a task-less technique. For thistechnique, at least one voxel within the image obtained by the fMRI isselected and a correlation analysis is performed to identify othervoxels that correspond with the selected voxel. However, it may bechallenging to identify which voxel to select. For example, anindividual would require a great deal of expertise and resources toselect a relevant voxel. In fact, selecting a voxel and/or processinginformation to select a voxel can be time consuming.

Accordingly, it is desirable to provide a system and method that canreadily identify the voxels to select and, at substantially the sametime, provide suitable results for accurate brain mapping.

BRIEF DESCRIPTION

In one aspect, a computing device for use in a system for mapping brainactivity of a subject generally comprises a processor. The processor isprogrammed to select a plurality of measurements of brain activity thatis representative of at least one parameter of a brain of the subjectduring a resting state. Moreover, the processor is programmed to compareat least one data point from each of the measurements with acorresponding data point from a previously acquired data set from atleast one other subject. The processor is also programmed to produce atleast one map for each of the measurements based on the comparison ofthe resting state data point and the corresponding previously acquireddata point. The processor may also be programmed to categorize the brainactivity in a plurality of networks in the brain based on the map.

In another aspect, a system for mapping brain activity of a subjectgenerally comprises a sensing system and a computing device that iscoupled to the sensing system. The sensing system is configured todetect a plurality of measurements of brain activity that isrepresentative of at least one parameter of a brain of the subjectduring a resting state. The computing device includes a communicationinterface that is configured to receive at least one signalrepresentative of the measurements, and a processor that is coupled tothe communication interface. The processor is programmed to select themeasurements of brain activity. Moreover, the processor is programmed tocompare at least one data point from each of the measurements with acorresponding data point from a previously acquired data set from atleast one other subject. The processor is also programmed to produce atleast one map for each of the measurements based on the comparison ofthe resting state data point and the corresponding previously acquireddata point. The processor may also be programmed to categorize the brainactivity in a plurality of networks in the brain based on the map.

In yet another aspect, a method for mapping brain activity of a patientgenerally comprises selecting, via a processor, a plurality ofmeasurements of brain activity that is representative of at least oneparameter of a brain of the subject during a resting state. At least onedata point from each of the plurality of measurements is compared, viathe processor, with a corresponding data point from a previouslyacquired data set from at least one other subject. At least one map isproduced, via the processor, for each of the measurements based on thecomparison of the resting state data point and the correspondingpreviously acquired data point. The brain activity is categorized, viathe processor, in a plurality of networks in the brain based on the map.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a block diagram of an exemplary system for task-less mappingof brain activity;

FIG. 2 is a block diagram of an exemplary computing device of the systemshown in FIG. 1 ;

FIG. 3 is flow diagram of an exemplary method for task-less mapping ofbrain activity using the system shown in FIG. 1 ;

FIG. 4 is an image of seed ROIs for generation of correlation map data;

FIG. 5 is a schematic of an exemplary standard multi-layer perceptronarchitecture and transfer function of the perceptron;

FIG. 6 is a graph of a learning rate;

FIG. 7 is a schematic of a voxel-wise classification;

FIGS. 8A-8D are graphs depicting correlation maps;

FIGS. 9A-9F are graphs depicting performance levels;

FIG. 10 is a graph depicting search space for perceptron architecture;

FIGS. 11A-11F are schematics of topographies in individual subjects;

FIGS. 12A-12C are schematics of classification results;

FIG. 13 is a scan of group averaged results;

FIGS. 14A and 14B are graphs of exemplary evaluations;

FIG. 15 is a scan of voxels; and

FIGS. 16A and 16B are scans of topography estimates.

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the U.S. Patent and TrademarkOffice upon request and payment of the necessary fee.

DETAILED DESCRIPTION OF THE DRAWINGS

The exemplary systems, apparatus, and methods described herein overcomeat least some known disadvantages associated with at least some knownbrain mapping techniques, such as task-based and/or task-less systems.More specifically, the embodiments described herein include a computingdevice for use in a system for mapping brain activity of a subject thatgenerally comprises a processor. The processor is programmed to select aplurality of measurements of brain activity that is representative of atleast one parameter of a brain of the subject during a resting state.Moreover, the processor is programmed to compare at least one data pointfrom each of the measurements with a corresponding data point from apreviously acquired data set from at least one other subject. Theprocessor is also programmed to produce at least one map for each of themeasurements based on the comparison of the resting state data point andthe corresponding previously acquired data point. The processor may alsobe programmed to categorize the brain activity in a plurality ofnetworks in the brain based on the map. By using previously acquireddata points to categorize the brain activity in a plurality of networksin the brain of the subject, task-based techniques can be avoided.Moreover, by having the processor select the plurality of measurements,a user may no longer need to spend a considerable amount of timedetermining which measurements, such as voxels, to select.

FIG. 1 illustrates an exemplary system 100 for mapping brain activity ofa subject (not shown). It should be noted that the term “brain activity”as used herein includes the various activities within a brain of thesubject that correspond to various tasks performed by the subject. Forexample, the brain transmits and receives signals in the form ofhormones, nerve impulses, and chemical messengers that enable thesubject to move, eat, sleep, and think. In the exemplary embodiment,system 100 is used to identify locations within a plurality of networkswithin the brain that are responsible for such brain activities.

As seen in FIG. 1 , system 100 includes a sensing system 102 that isconfigured to detect a plurality of measurements of brain activity thatis representative of at least one parameter of the brain of the subjectduring a resting state. In one suitable embodiment, sensing system 102is a magnetic resonance imaging device (MRI) that is configured togenerate at least one spectroscopic signal representative of a pluralityof measurements of brain activity that is representative of at least oneparameter of the brain of the subject during a resting state. Morespecifically, sensing system 102 may generate an altered magnetic fieldwithin the brain to measure various parameters of the brain. In anothersuitable embodiment, sensing system 102 may be a specialized MRI, suchas a functional magnetic resonance imaging (fMRI) device that is used tomeasure a variation in blood flow (hemodynamic response) related toneural activity in the brain or spinal cord (not shown) of the subject.In yet another suitable embodiment, sensing system 102 may be anelectrocorticography device having at least one electrode (not shown) tomeasure at least one voltage fluctuation within the brain. It should benoted that the present disclosure is not limited to any one particulartype of imaging and electrical technique or device, and one of ordinaryskill in the art will appreciate that the current disclosure may be usedin connection with any type of technique or device that enables system100 to function as described herein.

In the exemplary embodiment, system 100 also includes a computing device104 coupled to sensing system 102 via a data conduit 106. It should benoted that, as used herein, the term “couple” is not limited to a directmechanical, electrical, and/or communication connection betweencomponents, but may also include an indirect mechanical, electrical,and/or communication connection between multiple components. Sensingsystem 102 may communicate with computing device 104 using a wirednetwork connection (e.g., Ethernet or an optical fiber), a wirelesscommunication means, such as radio frequency (RF), e.g., FM radio and/ordigital audio broadcasting, an Institute of Electrical and ElectronicsEngineers (IEEE®) 802.11 standard (e.g., 802.11(g) or 802.11(n)), theWorldwide Interoperability for Microwave Access (WIMAX®) standard, ashort-range wireless communication channel such as BLUETOOTH®, acellular phone technology (e.g., the Global Standard for Mobilecommunication (GSM)), a satellite communication link, and/or any othersuitable communication means. IEEE is a registered trademark of theInstitute of Electrical and Electronics Engineers, Inc., of New York,N.Y. WIMAX is a registered trademark of WiMax Forum, of Beaverton, Oreg.BLUETOOTH is a registered trademark of Bluetooth SIG, Inc. of Kirkland,Wash.

In the exemplary embodiment, computing device 104 is configured toreceive at least one signal representative of a plurality ofmeasurements of brain activity from sensing system 102. Morespecifically, computing device 104 is configured to receive at least onesignal representative of an altered magnetic field within the brain ofthe subject from sensing system 102. Alternatively, computing device 104may be configured to receive at least one signal representative of atleast one voltage fluctuation within the brain from at least oneelectrode.

System 100 also includes a data management system 108 that is coupled tocomputing device 104 via a network 109. Data management system 108 maybe any device capable of accessing network 109 including, withoutlimitation, a desktop computer, a laptop computer, or other web-basedconnectable equipment. More specifically, in the exemplary embodiment,data management system 108 includes a database 110 that includespreviously acquired data of other subjects. In the exemplary embodiment,database 110 can be fully or partially implemented in a cloud computingenvironment such that data from the database is received from one ormore computers (not shown) within system 100 or remote from system 100.In the exemplary embodiment, the previously acquired data of the othersubjects may include, for example, a plurality of measurements of brainactivity that is representative of at least one parameter of a brain ofeach of the subjects during a resting state. Database 110 can alsoinclude any additional information of each of the subjects that enablessystem 100 to function as described herein.

Data management system 108 may communicate with computing device 104using a wired network connection (e.g., Ethernet or an optical fiber), awireless communication means, such as, but not limited to radiofrequency (RF), e.g., FM radio and/or digital audio broadcasting, anInstitute of Electrical and Electronics Engineers (IEEE®) 802.11standard (e.g., 802.11(g) or 802.11(n)), the Worldwide Interoperabilityfor Microwave Access (WIMAX®) standard, a cellular phone technology(e.g., the Global Standard for Mobile communication (GSM)), a satellitecommunication link, and/or any other suitable communication means. Morespecifically, in the exemplary embodiment, data management system 108transmits the data for the subjects to computing device 104. While thedata is shown as being stored in database 110 within data managementsystem 108, it should be noted that the data of the subjects may bestored in another system and/or device. For example, computing device104 may store the data therein.

During operation, while the subject is in a resting state, sensingsystem 102 uses a magnetic field to align the magnetization of someatoms in the brain of the subject and radio frequency fields tosystematically alter the alignment of this magnetization. As such,rotating magnetic fields are produced and are detectable by a scanner(not shown) within sensing system 102. More specifically, in theexemplary embodiment, sensing system 102 detects a plurality ofmeasurements of brain activity that is representative of at least oneparameter of the brain of the subject during the resting state. Sensingsystem 102 also generates at least one spectroscopic signalrepresentative of the plurality of measurements and transmits thesignal(s) to computing device 104 via data conduit 106. Moreover, dataof other subjects may be transmitted to computing device 104 fromdatabase 110 via network 109. As explained in more detail below,computing device 104 produces at least one map, such as a functionalconnectivity map, for each of the measurements based on a comparison ofat least one resting state data point of the subject and a correspondingdata point from the previously acquired data set from at least one othersubject. Computing device 104 uses the map to categorize or classify thebrain activity in a plurality of networks in the brain.

FIG. 2 is a block diagram of computing device 104. In the exemplaryembodiment, computing device 104 includes a user interface 204 thatreceives at least one input from a user, such as an operator of sensingsystem 102 (shown in FIG. 1 ). User interface 204 may include a keyboard206 that enables the user to input pertinent information. User interface204 may also include, for example, a pointing device, a mouse, a stylus,a touch sensitive panel (e.g., a touch pad, a touch screen), agyroscope, an accelerometer, a position detector, and/or an audio inputinterface (e.g., including a microphone).

Moreover, in the exemplary embodiment, computing device 104 includes apresentation interface 207 that presents information, such as inputevents and/or validation results, to the user. Presentation interface207 may also include a display adapter 208 that is coupled to at leastone display device 210. More specifically, in the exemplary embodiment,display device 210 may be a visual display device, such as a cathode raytube (CRT), a liquid crystal display (LCD), an organic LED (OLED)display, and/or an “electronic ink” display. Alternatively, presentationinterface 207 may include an audio output device (e.g., an audio adapterand/or a speaker) and/or a printer.

Computing device 104 also includes a processor 214 and a memory device218. Processor 214 is coupled to user interface 204, presentationinterface 207, and to memory device 218 via a system bus 220. In theexemplary embodiment, processor 214 communicates with the user, such asby prompting the user via presentation interface 207 and/or by receivinguser inputs via user interface 204. The term “processor” refersgenerally to any programmable system including systems andmicrocontrollers, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), programmable logic circuits (PLC),and any other circuit or processor capable of executing the functionsdescribed herein. The above examples are exemplary only, and thus arenot intended to limit in any way the definition and/or meaning of theterm “processor.”

In the exemplary embodiment, memory device 218 includes one or moredevices that enable information, such as executable instructions and/orother data, to be stored and retrieved. Moreover, memory device 218includes one or more computer readable media, such as, withoutlimitation, dynamic random access memory (DRAM), static random accessmemory (SRAM), a solid state disk, and/or a hard disk. In the exemplaryembodiment, memory device 218 stores, without limitation, applicationsource code, application object code, configuration data, additionalinput events, application states, assertion statements, validationresults, and/or any other type of data. Computing device 104, in theexemplary embodiment, may also include a communication interface 230that is coupled to processor 214 via system bus 220. Moreover,communication interface 230 is communicatively coupled to sensing system102 and to data management system 108 (shown in FIG. 1 ).

In the exemplary embodiment, processor 214 may be programmed by encodingan operation using one or more executable instructions and providing theexecutable instructions in memory device 218. In the exemplaryembodiment, processor 214 is programmed to select a plurality ofmeasurements that are received from sensing system 102 of brain activitythat is representative of at least one parameter of the brain of thesubject during a resting state. The plurality of measurements mayinclude, for example, a plurality of voxels of at least one image of thesubject's brain, wherein the image may be generated by processor 214within computing device 104. The image may also be generated by animaging device (not shown) that may be coupled to computing device 104and sensing system 102, wherein the imaging device may generate theimage based on the data received from sensing system 102 and then theimaging device may transmit the image to computing device 104 forstorage within memory device 218. Alternatively, the plurality ofmeasurements may include any other type measurement of brain activitythat enables system 100 to function as described herein.

Processor 214 may also be programmed to perform a correlation analysis.More specifically, in the exemplary embodiment, processor 214 may beprogrammed to compare at least one data point from each of the pluralityof measurements with a corresponding data point from a previouslyacquired data set from at least one other subject. For example,processor 214 may be programmed to compare a resting state data pointfrom each selected voxel from an image of the subject with acorresponding data point that is located within the same voxel of thepreviously acquired data set of the other subject. Processor 214 mayalso be programmed to produce at least one map (not shown in FIG. 2 ) ofthe brain of the subject, such as a functional connectivity map, foreach of the plurality measurements. The map is based on the comparisonof the resting state data point and the corresponding previouslyacquired data point. The map, for example, may illustrate the locationwithin the brain of a measured brain activity. Processor 214 may beprogrammed to produce the map by using the various compared data pointsin a known algorithm to calculate a plurality of outputs, such as, forexample, at least one output vector. One algorithm that may be used isrepresented in Equation 1 below.

$\begin{matrix}{{input}_{1} = {{\tanh}^{- 1}{\left( {\left\lbrack \frac{\ln\left( {1/{output}^{- 1}} \right)}{- a} \right\rbrack \cdot \frac{{{pi}{nv}}\left( {Weights}_{{hidden} - {output}} \right)}{a}} \right) \cdot \frac{{{pi}{nv}}\left( {Weights}_{{input} - {hidden}} \right)}{b}}}} & \left( {{Eq}.1} \right)\end{matrix}$

In Equation 1, a and b represent activating function parameters. Theoutput represents a seven dimensional output vector and pinv representsa pseudo inverse function.

Processor 214 may also be programmed to categorize or classify themeasured brain activity in a plurality of networks in the brain based onthe map. For example, processor 214 may be programmed to categorize themeasured brain activity to a particular neural network of the brain ofthe subject based on the location of the measured brain activity on themap of the subject's brain.

During operation, as the subject is in a resting state, sensing system102 detects a plurality of measurements of brain activity that isrepresentative of at least one parameter of the brain of the subject.Sensing system 102 transmits at least one signal representative of themeasurements to computing device 104 via data conduit 106. Morespecifically, the signals are transmitted to and received bycommunication interface 230 within computing device 104. Communicationinterface 230 then transmits the signals to processor 214 for processingand/or to memory device 218, wherein the data may be stored andtransmitted to processor 214 at a later time. Processor 214 may generatean image of the plurality of measurements. Alternatively, sensing system102 may transmit the signals to an imaging device (not shown), whereinan image of the measurements may be generated. The image may then betransmitted to computing device 104, wherein the image is stored withinmemory device 218 and transmitted to processor 214 for processing.

Moreover, data of other subjects may be transmitted to computing device104 from database 110 (shown in FIG. 1 ) via network 109 (shown in FIG.1 ). More specifically, the data may be received by communicationinterface 230 and then transmitted to processor 214 for processingand/or to memory device 218, wherein the data may be stored andtransmitted to processor 214 at a later time. Computing device 104 mayobtain the data at any time during operation.

In the exemplary embodiment, computing device 104 produces at least onemap for each of the plurality of measurements received. Morespecifically, processor 214 first selects each of the plurality ofmeasurements, received from sensing system 102. For example, in theexemplary embodiment, processor 214 selects each of the voxels from theimage. Alternatively, processor 214 may select any other types ofmeasurements for brain activity that enables system 100 to function asdescribed herein. Moreover, a user may see the image on the computingdevice 104, via presentation interface 207, and select the measurements,such as voxels, via user interface 204.

When each of the measurements has been selected, processor 214 thenperforms a correlation analysis. More specifically, processor 214compares at least one data point from each of the selected measurementswith a corresponding data point from a previously acquired data set fromat least one other subject, wherein computing device 104 obtained thedata set from database 110. For example, processor 214 may compare atleast one resting state data point from each selected voxel of the imageof the subject with a data point that is located within the same voxelof the previously acquired data set of at least one other subject.

When processor 214 has completed the correlation analysis, processor 214then produces at least one map (not shown in FIG. 2 ) of the brain ofthe subject, such as a functional connectivity map, for each of themeasurements. More specifically, processor 214 produces a map of thebrain of the subject based on each of the comparisons of each of theresting state data points and the corresponding previously acquired datapoints. The map, for example, may illustrate the location within thebrain of a measured brain activity. Processor 214 then categorizes orclassifies the measured brain activity in a plurality of networks in thebrain based on the map. For example, based on the location of themeasured brain activity in the map, processor 214 categorizes themeasured brain activity to a particular neural network of the brain ofthe subject. The map may be presented to the user via presentationinterface 207. Moreover, a textual representation and/or a graphicaloutput for the various categorizations may also be presented to the uservia presentation interface 207.

FIG. 3 is flow diagram of an exemplary method 300 for task-less mappingof brain activity of a brain of a subject using system 100 (shown inFIG. 1 ). A sensing system 102 (shown in FIG. 1 ) detects 302 aplurality of measurements of brain activity that is representative of atleast one parameter of the brain of the subject during a resting state.Sensing system 102 transmits 304 at least one signal representative ofthe measurements to a computing device 104 (shown in FIGS. 1 and 2 ).The signals are received 306 by a communication interface 230 (shown inFIG. 2 ). The measurements are selected 308 by a processor 214 (shown inFIG. 2 ). At least one data point from each of the measurements iscompared 310 with a corresponding data point from a previously acquireddata set from at least one other subject.

At least one map for each of the measurements is produced 312 based onthe comparison of the resting state data point and the correspondingpreviously acquired data point. The brain activity is categorized 314 ina plurality of networks in the brain based on the map. The map and/or anoutput for the categorization are displayed 316 to a user, via apresentation interface 207 (shown in FIG. 2 ).

The embodiments of the system and method for task-less mapping of brainactivity using resting state data of a brain of a subject, as describedherein, were used in the following exemplary experiment.

Experiment

In the exemplary experiment, perceptron training and testing used datasets previously acquired at the Neuroimaging Laboratories at theWashington University School of Medicine in St. Louis, Mo. All patientswere young adults screened to exclude neurological impairment andpsychotropic medications. Demographic information and acquisitionparameters are given in Table 1 below.

TABLE 1 Characteristics of the training test and validation data sets.Optimization Dataset Training (Test) Validation Number 21 (7M + 14 F) 17(8M + 9F) 10 (4M + 6F) of Subjects Age 27.6 (23-35) years 23.1 (18-27)years 23.3 ± 3 years Number 128 × 6 runs 194 × 4 runs 100 × 9 runs offrames TR (s) 2.16 2.16 3.03* *The TR in the validation data setincludes a one second pause between frames to accommodate simultaneousEEG recording.

In the exemplary experiment, all imaging was performed with a 3T Allegrascanner. Functional images were acquired using a BOLD contrast sensitivegradient echo echo-planar sequence [FOV=256 mm, flip angle=90°, 4 mm³voxels, other parameters listed in Table 1] during which subjects wereinstructed to fixate on a visual cross-hair, remain still, and not fallasleep. Anatomical imaging included one sagittal T1-weightedmagnetization prepared rapid gradient echo (MP-RAGE) scan (T1 W) and oneT2-weighted scan (T2 W).

Initial fMRI preprocessing followed conventional practice known in theart. This included compensation for slice-dependent time shifts,elimination of systematic odd-even slice intensity differences due tointerleaved acquisition, and rigid body correction for head movementwithin and across runs. Atlas transformation was achieved by compositionof affine transforms connecting the fMRI volumes with the T2 W and T1 Wstructural images. Head movement correction was included with the atlastransformation in a single resampling that generated volumetric timeseries in 3 mm³ atlas space. Additional preprocessing in preparation forcorrelation mapping included spatial smoothing (6 mm FWHM Gaussian blurin each direction), voxel-wise removal of linear trends over each fMRIrun, and temporal low-pass filtering retaining frequencies below 0.1 Hz.

Spurious variance was reduced by regression of nuisance waveformsderived from head motion correction and timeseries extracted fromregions (of “non-interest”) in white matter and CSF. Nuisance regressorsincluded also the BOLD timeseries averaged over the brain, i.e., globalsignal regression (GSR). Thus, all computed correlations wereeffectively order 1 partial correlations controlling for variance sharedacross the brain. GSR has been criticized on the grounds that itartificially generates anticorrelations. However, GSR fits well as astep preceding principal component analysis because it generatesapproximately zero-centered correlation distributions. As well, GSRenhances the spatial specificity in subcortical seed regions and reducesstructured noise. The question of whether the left tail of azero-centered correlation distribution (“anticorrelations”) is “false”or “tenuously interpretable” is irrelevant in the context of RSNclassification.

Correlation maps were computed using standard seed-based procedures,i.e., by correlating the timeseries averaged over all voxels within theseed (generally, 5 mm spheres) against all other voxels, excluding thefirst 5 (pre-magnetization steady-state) frames of each fMRI run.Frame-censoring was employed with a threshold of 0.5% RMS frame-to-frameintensity change. Frame-censoring excluded 3.8±1.1% of all magnetizationsteady-state frames from the correlation mapping computations.Correlation maps were Fisher z-transformed prior to further analyses.

In the exemplary embodiment, Cortical reconstruction and volumesegmentation were performed using FreeSurfer. Adequate segmentation wasverified by inspection of the FreeSurfer-generated results in each ofthe 21 training and 17 test datasets. Cortical and subcortical graymatter regions were selected from these segmentations, thresholded toobtain a conjunction of 30% of subjects, and then masked with an imageof the average BOLD signal intensity across all subjects, thresholded at80% of the mode value. This last step removes from consideration brainareas in which the BOLD signal is unreliable because of susceptibilityartifacts. The resulting 30,981 voxels constituted the grey matter mask.This mask was applied to all correlation maps input to the classifier.Individual surfaces were deformed to a common space, producingconsistent assignment of surface vertex indices with respect to gyralfeatures across subjects. Final volumetric results for each subject weresampled onto surface vertices by cubic spline interpolation ontomid-thickness cortical surface coordinates.

Seed regions were generated by meta-analyses of task-fMRI studies.Task-response foci were initially assigned to one of 10 functionalnetworks in Table 2 below. Each task fMRI study contributed a variablenumber of foci (Task ROIs column in Table 2). Task foci were used asseeds to generate correlation maps in all 21 subjects in the trainingset. These maps then were entered into random effects analyses (againstthe null hypothesis of no correlation) to produce Gaussianizedt-statistic (Z-score) images. Z-score images representing seeds assignedto the same RSN were averaged. Additionally, a conjunction imagerepresenting at least 70% of random effects images for a given network(after thresholding at |Z|>3) was produced. Averaged Z-score images weremasked to include only voxels contained in the conjunction. Peaks of theconjunction-masked average were selected as center coordinates for 6 mmspherical ROIs. Accordingly, the constraint employed was that all ROIswithin a given network must be separated by at least 12 mm. This processresulted in a large set of ROIs that were operationally treated asprovisional.

TABLE 2 Studies of functional co-activation used to generate seed ROIs.Final Task Provisional seed RSN Task paradigm fMRI contrast ROIs seedROIs ROIs DAN 1. Rapid Serial Visual 1. Cue Type × event time 10  28 28Presentation (RSVP) 2. Cue location × cue type × 2. Rapid Serial Visualevent time Presentation (RSVP) 3. Event time 3. Posner Cueing Task VANPosner Cueing Task Invalid vs. Valid 2 19 15 CO* Mixed design (10different tasks) Graph theoretic analysis* N/A 7 SMN Posner Cueing TaskTarget Period 11  37 39 AN Various auditory stimuli Stimulation vs.control 2 12 VIS Visual Localizer Peripheral 8 19 30 Foveal 2 12 FPC*Mixed design (10 different tasks) Graph theoretic analysis* N/A 11 12LAN Perceptual vs. Episodic Sentence Reading 13  17 13 Memory SearchParadigm DMN Perceptual vs. Episodic Memory Retrieval 4 42 32 MemorySearch Paradigm *Regions reported were themselves the result of ameta-analysis followed by refinement. Hence, these seeds were directlyused as provisional ROIs.

In the exemplary embodiment, the provisional ROI set was iterativelyrefined by maximizing the spatial concordance between the correlationmap obtained from each seed and the map obtained by pooling all seedswithin the RSN to which the seed was assigned. Pooled seed correlationmaps were computed by averaging the time series across all seedsassigned to each RSN. The single seed and the pooled seed maps wereaveraged across subjects. RSN concordance was assessed as the spatialcorrelation between the (subject-averaged) single seed and the(subject-averaged) pooled seed maps. Seeds were considered outliers iftheir concordance estimate was less than 1.5 times the inter-quartilerange below the median of all other seeds in the RSN. Outlier seeds werereassigned to the RSN of greatest concordance, unless they weremaximally concordant with the currently assigned RSN, in which case theywere removed entirely. After reassignment and outlier rejection, newindividual seed and pooled seed correlation maps were re-computed andthe process was iterated. Convergence (no reassignments or outlierrejections) was achieved in 7 iterations. The cingulo-opercular (CO)network did not survive iterative refinement, and most seeds werereassigned to the ventral attention network or removed. Similarly, theauditory network was subsumed into the sensorimotor network and theoriginally distinct foveal and peripheral visual networks were combinedinto a single (VIS) network.

Iterative refinement yielded 169 ROIs representing 7 RSNs with highintra- and low inter-network correlation, as shown in FIGS. 4 and 15 .To these were added a nuisance category consisting of 6 ROIs in CSFspaces. The latter enabled the classifier to separate correlationpatterns representing CSF vs. true RSNs. Computing correlation maps foreach of the 175 seed regions in all 21 training subjects produced 3,675images that were used as training data. Each image in the training setwas masked to include only grey matter voxels, producing a 3,675×30,981matrix. Similarly, 175.17=2,975 images were computed in the test dataset. Each image was assigned to one RSN (see the description ofiterative seed ROI refinement above and Table 2).

A multilayer perceptron was constructed to classify resting-state fMRIcorrelation maps into 7 canonical spatial patterns predefined asresting-state networks. The core of the perceptron is an artificialneural network that includes an input, hidden, and output layer, eachconsisting of some number of nodes fully connecting to the next layer(all-to-all feed-forward). Training samples (correlation maps from aparticular seed and subject) are passed into this feed-forward networkand the output is compared to the correct RSN label, as specified in thefMRI task meta-analysis. The error in this comparison is used to updatethe connections, or weights, between layers to increase the performanceof the classifier.

As an initial pre-processing step, the dimensionality of the input datawas reduced by using principal component analysis (PCA). Representingcorrelation maps in terms of eigenvectors provides efficientcomputation, well-conditioned weight matrices, and a free parameter torepresent the complexity of the input data (number of PCs). PCA wasperformed on the matrix of masked correlation images (21 subjects×175seeds=3,675 images×30,981 voxels for PCA). Each correlation map in thetraining (3,675 images) and the test (2,975 images) data sets were thenrepresented using a variable number of principal components (PCs).

The input layer received the correlation map training data as vectors inPCA space (the value of a given correlation map projected along aparticular PC). Thus, the number of input nodes was a free parameterthat depended on the number of PCs used to represent the data. Eachtraining example (a correlation map from a particular seed ROI/subjectpair) was associated with a desired output value, d_(o) (Eq. (7)),corresponding to the a priori RSN labels. The goal of the trainingprocess is to compare the output to these desired values, therebygenerating an error signal used to update connection weights. Theoverall transfer function of the perceptron (Eq. (2)) corresponds to thedetailed schematic of the propagation of inputs through the perceptron(FIG. 5 , see legend for symbol definitions).

$\begin{matrix}{y_{o} = {\varphi_{o}\left( {\sum\limits_{h}{{w_{ho}(\eta)} \cdot {\varphi_{h}\left( {\sum\limits_{i}{w_{ih} \cdot y_{i}}} \right)}}} \right)}} & (2)\end{matrix}$

The total input to each hidden node, v_(h), is determined by the sum ofall input nodes, weighted by the feed-forward connections (Eq. (3)).This sum is then transformed by the hidden layer activation function tocompute the output value of the hidden layer node, y_(h) (Eq. (4)).

$\begin{matrix}{v_{h} = {\sum\limits_{i}{w_{ih} \cdot y_{i}}}} & (3)\end{matrix}$ $\begin{matrix}{y_{h} = {{\varphi_{h}\left( v_{h} \right)} = {{a \cdot \tanh}\left( {b \cdot v_{h}} \right)}}} & (4)\end{matrix}$

The output layer nodes operate in the same manner as hidden layer nodes(Eqs. (5) and (6)):

$\begin{matrix}{{v_{o}(n)} = {\sum\limits_{i}{{w_{ho}(n)} \cdot {y_{h}(n)}}}} & (5)\end{matrix}$ $\begin{matrix}{y_{o} = {{\varphi_{o}\left( v_{o} \right)} = \frac{1}{1 + e^{{- a} \cdot v_{o}}}}} & (6)\end{matrix}$

The After propagation of the input data through the perceptron, theoutput value for each node, y_(o), was compared to the desired value,d_(o), to find the error, e_(o) (Eq. (7)).

e _(o)(k)=d _(o)(k)−y _(o)(k)  (7)

The local gradient of the error at an output node is found by theproduct of this error and the inverse of the activating function appliedto the output value:

δ_(o) =e _(o)·φ_(o)′(v _(o))  (8)

where the prime notation indicates the first derivative. After everyiteration (n), the weights for the hidden to output layer connectionswere adjusted in the direction opposite of the gradient of the error:

w _(ho)(k+1)=w _(ho)(k)+η(k)·δ_(o)(k)·y _(h)(k)  (9)

where η is the learning rate, y_(h) is the value of hidden layer node h,and δ_(o) is local error gradient at output node o. Similarly, theweights to the hidden layer from the input layer, w_(ih), are adjustedaccording to Eq. (10).

w _(ih)(k+1)=w _(ih)(k)+η(k)·δ_(h)(k)·y _(i)(k)  (10)

The local gradient at a hidden node, δ_(h), may be computed byback-propagation from the output layer.

$\begin{matrix}{\delta_{h} = {{- \frac{dE}{d\gamma_{h}}} = {{\varphi_{h}^{\prime}\left( v_{h} \right)} \cdot {\sum\limits_{o}{\delta_{o} \cdot w_{ho}}}}}} & (11)\end{matrix}$

The learning rate parameter, η, was set empirically. A range of stablevalues was determined for a constant η, where instability was noted asdivergence or rapid oscillation of classifier weights. The presentresults were obtained using an adaptive learning rate that increased asa sigmoid in log iteration index (FIG. 6 ). The initial learning ratewas small (η(0)=A=5·10⁻⁴) to allow the classifier to begin a gentledescent in error gradient towards a stable solution. The learning rateincreased exponentially (B=−3,

=0.5), until saturating at an empirically determined upper limit ofstability to (η(∞)=2·10⁻³).

Separation of classes was quantified using receiver operatorcharacteristic analysis. Across a range of thresholds, the proportion ofwithin-class output values above the threshold (true positive fraction,TPF) were compared to the number of out-of-class values above thethreshold (false positive fraction, FPF). The TPF as a function of FPFdefines the ROC curve. The area under the ROC curve (AUC) was used as asummary statistic of classification performance for each RSN class.

At logarithmically spaced intervals during the training process,training was paused and AUCs were calculated in a separate test dataset. This procedure produced training trajectories indicating therelative performance for each RSN (FIG. 9D) throughout the trainingprocess. Peak performance for a given RSN was defined at the iterationproducing the maximum AUC value in the test data (FIG. 9D). Overallperformance was calculated as the average of AUC values across networks(FIG. 9D).

In the exemplary embodiment, the number of PCs sampled (N_(i)), and thenumber of nodes in the hidden layer (N_(h)) constitute hyper-parameterssubject to optimization. Overall RMS error was evaluated over a denselysampled N_(i)∈[5, 6600]×N_(h)∈[4, 5000] space. For each (N_(t), N_(h))coordinate, a classifier was trained until test set error reached aminimum. The architecture with the least error (minimum of eightrepetitions for each coordinate) was selected (FIG. 10 ).

After identifying the architecture with least error in the test dataset, performance was further optimized by simulated annealing,countering the tendency of perceptrons to become trapped in localminima. Mimicking the random movement of atoms aligning in coolingmetal, simulated annealing uses random perturbations of model parametersto find the global extremum in an objective function. Perturbations ofsteadily decreasing size (specified by a ‘cooling profile’) areguaranteed to find a global minimum with slow enough cooling, although,in practice, the necessary cooling profile is prohibitively slow. Aftertraining the perceptron until a minimum in RMS test set error, everyweight, {w_(ih)} and {w_(ho)}, was multiplied by a random coefficient.Training was then resumed to find a new minimum. If lower error wasachieved, the new weights were accepted. This process was then repeated.

The value for each weight was determined by first sampling from auniform distribution, x∈[−1, 1], transformed by a hyperbolic function,N=(1−x)/(1+x). Thus each weight was multiplied by N∈[0, ∞], and was thusunchanged when x=0. The range sampled within x determined the amount ofnoise injected into the system, using values closer to zero over thecourse of cooling. The maximum value of x was determined by thetemperature, T, and the minimum value was determined so that the meansquared value of N was unity:

$\begin{matrix}{{\frac{1}{T - a}{\int_{a}^{T}{\left( \frac{x - 1}{x + 1} \right)^{2}{dx}}}} = 1} & (12)\end{matrix}$

This choice of noise ensured that the sum of squares of the connectionweights was unaltered by perturbation and that most weights weredecreased, while a small selection was sporadically increased. Ageometric cooling function (Eq. (13)) was used, which decreased over K₁perturbation epochs; this entire annealing process was repeated K₂times, each time with a slightly cooler temperature profile.

T _(k) ₁ _(,k) ₂ =T ₀ ·r ^((k) ¹ ^(+3k) ² ⁾  (13)

The following parameters were used: r=0.95, T₀=0.4, K₁=40, K₂=20. To mapRSNs in individual subjects, a correlation map was generated for everyvoxel in the brain and then classified using the trained and optimizedperceptron. An overall schematic of this process is depicted in FIG. 10. A correlation map was produced for every point in the brain bycorrelating every voxel's BOLD time-course with every other voxel in thebrain. Each map was masked before classification to include only greymatter voxels producing a 65,549 (voxels within brain mask)×30,981(voxels within grey matter mask) element matrix (FIG. 7 ). This data wasthen projected onto the eigenvectors of the training data, reducing thedimensionality to 65,549×2500 (FIG. 7 ). Thus, all correlation maps wererepresented in the same input data space for classifier training andtesting. The reduced whole-brain connectivity data was then propagatedthrough the perceptron, with the first layer reducing the data to 22features (65,549×22; FIG. 7 ), and the second layer producing RSNestimates (65,549×8, FIG. 7 ). However, FIG. 7 depicts only 7 outputclasses because one of the 8 outputs is a nuisance component used onlyin post-processing.

Classifier output values are approximately uniformly [0, 1] distributedas a result of the logistic activation function on the output layer (Eq.(6)). Classifier values were then normalized within each voxel to sum tounity (FIG. 7 ). This normalization penalized voxels that had highclassification values for multiple networks. The presence of a CSFclassification component further penalized RSN estimates in voxelsexhibiting CSF-related correlation patterns. Within each network,classifier values were then converted back to an exactly uniform [0, 1]distribution across voxels (rank-order transform). This transformationresulted in voxels ranked in membership for each network across thebrain expressed as a percentile.

To visualize group level results on the cortical surface, RSN topographyestimates were projected to the cortical mid-thickness surface for eachsubject (after surface-registration across subjects). Averages were thencomputed across surface nodes. The standard deviation of classifiervalues was also calculated node-wise to illustrate regions of highvariability. These group-level results were projected onto thegroup-average inflated surface. To visualize group level results insub-cortical structures, classifier values were averaged voxel-wiseacross subjects. Group-average images were then re-sampled to 1 mm cubicvoxels and overlaid on a co-registered MNI152 atlas target.

In the exemplary embodiment, spatial correlation analysis (FIG. 8B) andprincipal component analysis (FIG. 8C) of the training data (thecorrelation maps produced for each seed ROI) revealed distinctclustering corresponding to RSNs. In the map-to-map spatial correlationmatrix (averaged across subjects), training inputs showed highcorrelation with other inputs of the same RSN compared to inputs ofother classes (FIG. 8B). Additionally, the map-to-map correlation matrixshowed two major clusters, one corresponding to the DAN, VAN, VIS, andMOT networks, and the other corresponding to the FPC, LAN, and DMNnetworks. Projection of all 3,675 correlation maps into principalcomponent space gave rise to partially overlapping clusterscorresponding to 7 RSNs. In the PC1×PC2 plane (FIG. 6 ), DAN (purple)and DMN (red) showed little overlap and appeared at opposite ends of thePC1 axis. MOT (light blue) and VIS (green) clusters were highlyoverlapping in this plane, but showed little overlap in the PC3×PC4plane.

FIGS. 9A-9F shows the training performance for the perceptron optimizedfor overall performance (2500 input PCs, 22 hidden layer nodes). Forevery correlation map, the perceptron output node values represent anestimate of membership for each RSN. The expectation value of allinitial perceptron outputs is 0.5 (FIGS. 9A-9C) as the expected outputvalue with zero-mean weights (v_(o)) is 0 (Eqs. (5) and (6)). Astraining progresses, within-class output values increase towards unity(e.g., DMN output node values for DMN inputs, red traces in FIG. 9A),while out-of-class output values decrease towards zero (DMN output valuefor non-DMN ROI-derived maps, all other traces in FIG. 9A).

Area under the ROC curve (AUC) trajectories are shown in FIG. 9D. Thisquantity, averaged across RSN classes, began near chance (0.65 after oneiteration) and rose in later iterations. For all networks, the AUCexhibited a transient decrement in performance early in training. Thisfeature corresponded to transient changes of slope in RMS error but didnot produce concavity (local minima) in RMS error (FIG. 9E). Classseparation was achieved at varying numbers of iterations for differentRSNs. Across all perceptron architectures, the default mode network (redtrace) always achieved asymptotic performance earliest, and the languagenetwork (orange) latest. Asymptotic performance for CSF classificationoccurred much later than any true RSN. Performance on the test datainitially followed training performance until reaching a global maximum(FIG. 9E). This maximum occurred at varying iteration indices fordifferent RSNs. Training beyond this point resulted in over-fitting,manifesting as decreasing test data performance despite increasingtraining performance. Inputs that were previously correctly classifiedin the test data became incorrectly classified (FIGS. 9B and 9C).

Over a dense sampling of input and hidden layer sizes (N_(i)×N_(h)), theperceptron was trained until the peak AUC could be determined (FIG. 10). The optimal overall performance for the perceptron was found at 2500PCs and 22 hidden layer nodes (FIG. 10 ). The perceptron was trainedwith this architecture using 10 mm ROIs and the result was optimizedthrough simulated annealing, yielding an over-all classificationperformance of 0.9822 (AUC) with 17.1% RMS error. The maximal AUC andminimal RMS error rates differed by network, as shown in Table 3.

TABLE 3 RSN classification performance. Test Retest (Optimization Set)(Validation Set) Accuracy Error Accuracy Error Network (AUC) (RMS) (AUC)(RMS) DAN 0.973 20.2% 0.973 20.1% VAN 0.971 17.9% 0.979 17.6% SMN 0.98816.4% 0.994 17.2% VIS 0.993 13.4% 0.998 12.7% FPC 0.972 17.5% 0.98914.8% LAN 0.985 14.9% 0.991 14.4% DMN 0.993 14.4% 0.990 17.6% Mean 0.98217.1% 0.988 16.6%

These values reflect MLP training with 10 mm radius seeds (FIGS. 14A and14B) and optimization with simulated annealing. After completion ofclassifier training, voxel-wise connectivity patterns were classified inindividual subjects in the test data set (FIG. 11A). RSN topographysummaries were computed as winner-take-all maps (FIGS. 11A-11F and12A-12C). Well-defined RSN topography was obtained in all subjects inthe test and training groups. Specifically, the subject-wise mean andstandard error of the AUC was 0.982±0.007, with the worst performingsubject at 0.963. These figures corresponded to RMS error of 16.5±1.4%with the worst subject yielding 19.1%. RSNs were generally contiguousregions that conformed to previously described topography. Therelationship of perceptron-defined RSNs to previous findings isdiscussed more fully below.

RSN topography estimates were averaged over all subjects in the trainingand test groups. FIGS. 12A-12C addresses both the central tendency (toprow) of each group as well as inter-subject variability (middle row).Average network topographies had higher values near locations of ROIsused to generate training maps. This is expected because voxels withinROIs are likely to have similar correlation maps to the ROI. Highclassification values (in the top 25%) were also found in contiguousregions not used to generate training data. For example, a lateraltemporal region was classified as a fronto-parietal control region, anda dorsal pre-motor region was classified into the language network. Thistype of result demonstrates external validity (or equivalently,generalizability) of perceptron classification, i.e., recovery of truefeatures in RSNs not included in the training set. These features arealso present in the results in individual subjects (FIG. 11A).

Further evidence of external validity is shown in FIG. 13 . For example,thalamic voxels approximately corresponding to nucleus ventralisposterior were classified as SMN, substantially in agreement. Similarly,voxels in the posterior cerebellum (Crus I and II) and the cerebellartonsils were classified as DMN (FIG. 13 , Z=−30, Z=−47), substantiallyin agreement. These results are notable because neither cerebellar northalamic ROIs were used to generate training data. Further, nocerebellar voxels were within the grey matter mask, which means that theclassifier successfully identified cerebellar ROIs purely on the basisof cortical connectivity maps. The perceptron generated asymmetricclassification in the cerebellum for the VAN and LAN networks (Z=−30),contralateral to their asymmetric cerebral representation (Z=+47).

The present results (individual and group RSN topographies) exhibit ahigh degree of face validity with respect to the training data andpreviously reported RSN results (FIGS. 11A-11F, 12A-12C, and 13 ). Thus,for example, components of the DMN used as seeds to generate thetraining data were classified as DMN in all subjects. This was true notonly for easily classified networks (e.g., the DMN) but also fornetworks (e.g., VAN and LAN) that are inconsistently found byunsupervised procedures. The results shown in FIGS. 11A-11F illustratethat the perceptron reliably classified RSNs in each test set individual(AUC>0.971), even in cases in which the RMS error was relatively high(>0.2).

However, inter-individual differences were also evident (FIGS. 11A-11F).These differences systematically varied according to RSN and exhibitedRSN-specific zones of high as well as low inter-individual variability(FIGS. 12A-12C). Easily classified voxels (i.e., with highclassification values) generally showed the least inter-subjectvariability. Such regions, e.g., the posterior parietal component of theDMN (FIGS. 12A-12C), were surrounded by zones of high variability (e.g.,ring around the angular gyms). The pre- and post-central gyriconsistently showed high SMN classification values but were bordered byregions of high inter-subject SMN variability. Interestingly,inter-subject variability was low also in areas with classificationvalues near 0, particularly in areas typically anticorrelated with othernetworks (e.g., low DAN variance in the angular gyms, a component of theDMN; low DMN variance in MT+, a component of the DAN).

At least four factors potentially contribute to observed inter-subjectclassifier output variability: (i) limited or compromised fMRI data,(ii) limitations intrinsic to the MLP (iii) true inter-individualdifferences in RSN topography and (iv) misregistration. Each of thepossibilities is discussed below.

With regard to (i), the fMRI data used in the present work were obtainedin healthy, cooperative young adults. Hence, the fraction of framesexcluded because of head motion was low (about 4%). The total quantityof fMRI data acquired in each individual was, by current standards,generous. However, fMRI data quantity clearly affects MLP performance(see 4.4.2 below and FIGS. 14A-14B). Current results suggest that moredata generally improves MLP performance. The impact of fMRI data qualityand quantity on MLP performance in clinical applications remains to bedetermined. With regard to (ii), the observation of zones with highclassifier values and low variance bordered by regions of high variancemay reflect classification uncertainty in areas that truly representmore than one RSN, i.e., voxels with high participation coefficients.

With regard to (iii), on the other hand, the presently observedinter-individual differences may truly reflect individual variability inRSN topography. Previous work has demonstrated that inter-individualdifferences in task-evoked activity correspond to “transition zones” inresting state networks (e.g., the boundary between parietal DMN and DANregions). These same regions appear in our inter-subject variance mapsfor both DMN and DAN (FIGS. 12A-12C). We also note that areas of highRSN classification variability (pre-frontal, parietal, lateral temporal)broadly correspond to regions exhibiting the greatest expansion over thecourse of human development and evolution. This correspondence maypossibly be coincidental, but it is consistent with the hypothesis thatlater developing or evolutionarily more recent areas of the brain tendto be more variable across individuals.

With regard to (iv), some proportion of the variability in observed RSNtopography estimates may be explained by uncorrected anatomicalvariability. To investigate this possibility, the overall RSN standarddeviation map (FIG. 16A) was compared to sulcal depth variability (FIG.16B) and a weak spatial correlation (r=0.2) was found. By inspection,these maps were concordant only at a broad spatial scale: both showedlow variability in primary motor/auditory/insular cortices and highvariability elsewhere. Little correspondence was evident at finer scales(note lack of annular patterns in FIG. 16B). The degree to whichanatomical variability contributes to spurious variance in RSNtopography estimates may be addressed by measuring the degree to whichnon-linear or surface-based registration decreases inter-subjectvariance and increases overall classifier performance (higher AUC, lowerRMSE).

Two distinct types of external validity, that is, correct classificationoutside the training set, are evident in our results. First, highoverall classification performance was achieved for a priori seed-basedcorrelation maps in test (98.2% AUC) and hold-out datasets (98.8% AUC).Performance was reliable in all subjects (97.1% worst-case AUC), whichis critical in clinical applications. Second, and perhaps of greaterscientific interest, the RSN estimates in areas not covered by seedregions were strongly concordant with previously reported task-based andresting-state fMRI results. For example, while no temporal FPC seed ROIwas included in the training set, a posterior temporal gyms locus wasclassified as FPC the group level (FIGS. 12A-12C). Similarly, the MLPalso identified the parahippocampal gyms as DMN and a dorsal pre-motorregion that has been associated with articulation of speech as LAN. Theright inferior cerebellum was first associated with language function byPET studies of semantic association tasks. Identification of this regionhere as part of the LAN network (FIG. 13 , WTA, Z<−30) is doublysignificant. First, no cerebellar seeds were used to generate trainingdata and, further, cerebellar voxels were excluded from the gray mattermask, hence, were not seen by the classifier. Second, lateralized RSNcomponents typically are not found by unsupervised seed-basedcorrelation mapping.

These findings highlight the capabilities of supervised classifiersapplied to the problem of identifying RSNs in individuals. Therepresentation of language (primarily Broca's and Wernicke's areas) hasbeen extensively studied using task-based fMRI and correlation mappingwith a priori selected ROIs. However, the language network, as presentlydefined, typically is not recovered as such by unsupervised methods.Rather, components of the LAN are generally found only at fine-scale RSNdescriptions. Thus, an RSN including Broca's and Wernicke's areasappears as the 11th of 23 components in; these same areas wereidentified as VAN and DMN. A component consistent with the presentlydefined LAN at a hierarchical level of 11 (but not 7) clusters has beenfound. Thus, the exemplary experiment, work demonstrates the potentialof supervised classifiers to find networks that are subtle features ofthe BOLD correlation structure, possibly even minor sub-componentswithin hierarchically organized RSNs, that nevertheless have highscientific and/or clinical value. The LAN was specifically included hereto meet the clinical imperative of localizing language function in thecontext of pre-operative neurosurgical planning.

In the exemplary embodiment, the hierarchical scale of an RSN isreflected in training performance trajectories (FIGS. 9E and F): in all(N_(i)×N_(h)) architecture variants, the DMN was the first to beseparated from other RSNs. The DMN arguably is the most robust featurein the correlation structure of intrinsic brain activity. Its topographyis very similar across RSN mapping strategies (specifically, spatial ICAand seed-based correlation mapping. Here, the DMN and regionsanticorrelated with the DMN were well separated along the firstprincipal component of the training data (FIG. 6 ).

After the DMN, the sensorimotor and visual networks were next to achieveseparation during classifier training. These networks are often seen atthe next level down in the RSN hierarchy as offshoots of the anti-DMN orextrinsic system. The dorsal attention network achieved only a smallpeak in error descent compared to other ‘extrinsic’ networks, thoughthis occurred in close proximity (note overlap of DAN, MOT, VIS peaks inFIG. 9F). In contrast, the LAN and VAN were last to achieve separationduring training. This corresponds to the observation that LAN and VANsystems are typically found by analyses extending to lower levels of theRSN hierarchy.

In the exemplary embodiment, the observer is a multi-layer perceptronand the task is to assign RSN labels to each voxel. Performance isevaluated in terms of mean squared classification error and ROCanalyses. It follows that MLP performance can be used to evaluate imagequality across a wide range of variables, e.g., scanners, andacquisition parameters (e.g., TR, run length, resolution), preprocessingstrategies (nuisance regression, filtering, spatial smoothing) and datarepresentations (surface or volume based). This principle isdemonstrated by systematically evaluating MLP performance in relation toquantity of fMRI data and seed ROI size.

The relation between total quantity of fMRI data and MLP performance(test dataset RMS error) is shown in FIG. 14A. The plotted pointsrepresent five replicate MLP training/test runs. RMS error as a functionof data quantity was well fit (R²=0.994) by a three-parameterempirically derived hyperbolic function. The parameterized functionimplies that classifier error monotonically decreases with increasingtotal fMRI data length but ultimately asymptotes at ˜18% RMS (with 5 mmradius seeds and no simulated annealing). The existence of thisasymptote may indicate that resting-state brain networks are inherentlynon-separable in the sense of classification. This is consistent withthe notion of “near decomposability” of hierarchical systems formed bymultiple, sparsely inter-connected modules. This concept has since beenextended to brain networks.

The relationship between seed ROI radius and RMS classification errorwas explored using a perceptron architecture optimized with 5 mm radiusseeds (2500 PCs, 22 hidden nodes). All seeds were masked to include onlygray matter voxels. The results of systematically varying seed ROI sizeare shown in FIG. 14B. A clear minimum in RMS error was obtained withseeds of approximately 10 mm radius. Voxel-wise RSN topographies werequalitatively similar across ROI sizes, but larger seeds generated lessnoisy RSNs with more pronounced peaks. This result is unexpected, as itdeviates from the current standard practice of using approximately 6 mmradius seeds. There are several possible explanations for the presentresults. Large seeds may best match the characteristic dimensions ofRSNs in the 7-network level description of the brain. Alternatively,large seeds may compensate for misregistration in affine-coregistered,volume-preprocessed data. Smaller seeds may be used in classifiersoperating on non-linear, surface-coregistered, geodesically smootheddata. The results shown in FIG. 14B reflect the effect of seed radius onthe correlation maps used to train the MLP. It is formally possible fora corrupted training set to yield a better classifier as evaluated bytest set classification error. Thus, the results shown in 14B should notbe interpreted as unambiguously indicating that 10 mm radius seeds areoptimal for correlation mapping.

Inter-individual differences in computed RSN topographies may reflectmultiple factors. Cross-gyral contamination due to the relatively largevoxels used in this study (4 mm acquisition, 3 mm post-processinganalyses) may limit the precision of RSN classification in the dataset.Potential strategies for validating perceptron-derived results includecomparison with measures of structural (axonal) connectivity andinvasive electrophysiologic recording.

The MLP RSN classifier operates at the voxel level via computedcorrelation maps. After training, it reliably identifies RSNtopographies in individual subjects. Classification is rapid (2 minutesusing Matlab running on Intel i7 processors) and automated, hencesuitable for deployment in clinical environments. After training,classification is independent of any particular seed. Therefore, thetrained MLP is expected to be robust to anatomical shifts anddistortions, for example, owing to enlarged ventricles and mass effectsor even loss of neural tissue (e.g., stroke).

In this experiment, the classifier was trained to operate in 3D imagespace for compatibility with clinical imaging formats. However, the MLPconcept can be readily adapted to operate on correlation mapsrepresented on the cortical surface. Similarly, voxel-wise classifierscan be trained to classify subjects despite anatomical abnormalities(e.g., brain tumors) by altering the domain of the training set, i.e.,excluding tumor voxels. Another potentially useful MLP modificationwould be removal by regression of the relationship between correlationand distance to the seed. Such regression may decrease the reliance ofthe classifier on local connectivity, thereby reducing susceptibility tocorruption by movement artifact.

As compared to known systems that are used for brain mapping, theembodiments described herein enable a substantially efficient task-lesssystem for brain mapping. More specifically, the embodiments describedherein include a computing device for use in a system for mapping brainactivity of a subject that generally comprises a processor. Theprocessor is programmed to select a plurality of measurements of brainactivity that is representative of at least one parameter of a brain ofthe subject during a resting state. Moreover, the processor isprogrammed to compare at least one data point from each of themeasurements with a corresponding data point from a previously acquireddata set from at least one other subject. The processor is alsoprogrammed to produce at least one map for each of the measurementsbased on the comparison of the resting state data point and thecorresponding previously acquired data point. The processor may also beprogrammed to categorize the brain activity in a plurality of networksin the brain based on the map. By using previously acquired data pointsto categorize the brain activity in a plurality if networks in the brainof the subject, task-based techniques may be avoided. Moreover, byhaving the processor select the plurality of measurements, a user may nolonger need to spend a considerable amount of time determining whichmeasurements, such as voxels, to select.

Exemplary embodiments of the system, apparatus, and method are describedabove in detail. The system, apparatus, and method are not limited tothe specific embodiments described herein, but rather, components of thesystem and apparatus, and/or steps of the methods may be utilizedindependently and separately from other components and/or stepsdescribed herein. For example, but not limited to, the system may alsobe used in combination with other apparatus, systems, and methods, andis not limited to practice with only the system as described herein.Rather, the exemplary embodiment can be implemented and utilized inconnection with many other applications.

Although specific features of various embodiments of the invention maybe shown in some drawings and not in others, this is for convenienceonly. In accordance with the principles of the invention, any feature ofa drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

Although described in connection with an exemplary computing systemenvironment, embodiments of the invention are operational with numerousother general purpose or special purpose computing system environmentsor configurations. The computing system environment is not intended tosuggest any limitation as to the scope of use or functionality of anyaspect of the invention.

Embodiments of the invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices. The computer-executableinstructions may be organized into one or more computer-executablecomponents or modules. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks or implement particular abstract datatypes. Aspects of the invention may be implemented with any number andorganization of such components or modules. For example, aspects of theinvention are not limited to the specific computer-executableinstructions or the specific components or modules illustrated in thefigures and described herein. Other embodiments of the invention mayinclude different computer-executable instructions or components havingmore or less functionality than illustrated and described herein.Aspects of the invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

In operation, a computer executes computer-executable instructionsembodied in one or more computer-executable components stored on one ormore computer-readable media to implement aspects of the inventiondescribed and/or illustrated herein.

The order of execution or performance of the operations in embodimentsof the invention illustrated and described herein is not essential,unless otherwise specified. That is, the operations may be performed inany order, unless otherwise specified, and embodiments of the inventionmay include additional or fewer operations than those disclosed herein.For example, it is contemplated that executing or performing aparticular operation before, contemporaneously with, or after anotheroperation is within the scope of aspects of the invention.

When introducing elements of aspects of the invention or the embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

What is claimed is:
 1. A computer-implemented method for mapping aresting brain activity of an individual subject at a plurality ofpositions within the brain of the individual subject, the methodcomprising: receiving, at a computing device, a plurality of correlationmaps, each correlation map comprising a plurality of elements, eachelement comprising a correlation between one time-series measurement andan additional time-series measurement selected from of a plurality oftime-series measurements, the one time-series measurement obtained fromone location within the brain of the individual subject during a restingstate, and the additional time-series measurement obtained from one of aplurality of additional locations within the brain of the individualsubject during a resting state; transforming, using the computingdevice, each correlation map to a classification of brain activity usinga predetermined supervised classifier; and mapping, using the computingdevice, each classification of brain activity transformed from eachcorrelation map to the one location within the brain of the individualsubject for each correlation map.